mirror of
https://github.com/opencv/opencv.git
synced 2024-12-05 09:49:12 +08:00
2bbda9d225
Conflicts: modules/cudaimgproc/test/test_color.cpp modules/dynamicuda/include/opencv2/dynamicuda/dynamicuda.hpp modules/gpu/perf/perf_imgproc.cpp modules/gpu/src/imgproc.cpp modules/gpu/test/test_core.cpp modules/gpu/test/test_imgproc.cpp modules/java/generator/src/cpp/VideoCapture.cpp samples/gpu/performance/CMakeLists.txt samples/tapi/CMakeLists.txt
154 lines
6.0 KiB
C++
154 lines
6.0 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "test_precomp.hpp"
|
|
|
|
|
|
TestHaarCascadeLoader::TestHaarCascadeLoader(std::string testName_, std::string cascadeName_)
|
|
:
|
|
NCVTestProvider(testName_),
|
|
cascadeName(cascadeName_)
|
|
{
|
|
}
|
|
|
|
|
|
bool TestHaarCascadeLoader::toString(std::ofstream &strOut)
|
|
{
|
|
strOut << "cascadeName=" << cascadeName << std::endl;
|
|
return true;
|
|
}
|
|
|
|
|
|
bool TestHaarCascadeLoader::init()
|
|
{
|
|
return true;
|
|
}
|
|
|
|
|
|
bool TestHaarCascadeLoader::process()
|
|
{
|
|
NCVStatus ncvStat;
|
|
bool rcode = false;
|
|
|
|
Ncv32u numStages, numNodes, numFeatures;
|
|
Ncv32u numStages_2 = 0, numNodes_2 = 0, numFeatures_2 = 0;
|
|
|
|
ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages);
|
|
ncvAssertReturn(h_HaarStages.isMemAllocated(), false);
|
|
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes);
|
|
ncvAssertReturn(h_HaarNodes.isMemAllocated(), false);
|
|
NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures);
|
|
ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false);
|
|
|
|
NCVVectorAlloc<HaarStage64> h_HaarStages_2(*this->allocatorCPU.get(), numStages);
|
|
ncvAssertReturn(h_HaarStages_2.isMemAllocated(), false);
|
|
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes_2(*this->allocatorCPU.get(), numNodes);
|
|
ncvAssertReturn(h_HaarNodes_2.isMemAllocated(), false);
|
|
NCVVectorAlloc<HaarFeature64> h_HaarFeatures_2(*this->allocatorCPU.get(), numFeatures);
|
|
ncvAssertReturn(h_HaarFeatures_2.isMemAllocated(), false);
|
|
|
|
HaarClassifierCascadeDescriptor haar;
|
|
HaarClassifierCascadeDescriptor haar_2;
|
|
|
|
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
|
|
NCV_SKIP_COND_BEGIN
|
|
|
|
const std::string testNvbinName = cv::tempfile("test.nvbin");
|
|
ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
ncvStat = ncvHaarStoreNVBIN_host(testNvbinName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
ncvStat = ncvHaarGetClassifierSize(testNvbinName, numStages_2, numNodes_2, numFeatures_2);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
ncvStat = ncvHaarLoadFromFile_host(testNvbinName, haar_2, h_HaarStages_2, h_HaarNodes_2, h_HaarFeatures_2);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
NCV_SKIP_COND_END
|
|
|
|
//bit-to-bit check
|
|
bool bLoopVirgin = true;
|
|
|
|
NCV_SKIP_COND_BEGIN
|
|
|
|
if (
|
|
numStages_2 != numStages ||
|
|
numNodes_2 != numNodes ||
|
|
numFeatures_2 != numFeatures ||
|
|
haar.NumStages != haar_2.NumStages ||
|
|
haar.NumClassifierRootNodes != haar_2.NumClassifierRootNodes ||
|
|
haar.NumClassifierTotalNodes != haar_2.NumClassifierTotalNodes ||
|
|
haar.NumFeatures != haar_2.NumFeatures ||
|
|
haar.ClassifierSize.width != haar_2.ClassifierSize.width ||
|
|
haar.ClassifierSize.height != haar_2.ClassifierSize.height ||
|
|
haar.bNeedsTiltedII != haar_2.bNeedsTiltedII ||
|
|
haar.bHasStumpsOnly != haar_2.bHasStumpsOnly )
|
|
{
|
|
bLoopVirgin = false;
|
|
}
|
|
if (memcmp(h_HaarStages.ptr(), h_HaarStages_2.ptr(), haar.NumStages * sizeof(HaarStage64)) ||
|
|
memcmp(h_HaarNodes.ptr(), h_HaarNodes_2.ptr(), haar.NumClassifierTotalNodes * sizeof(HaarClassifierNode128)) ||
|
|
memcmp(h_HaarFeatures.ptr(), h_HaarFeatures_2.ptr(), haar.NumFeatures * sizeof(HaarFeature64)) )
|
|
{
|
|
bLoopVirgin = false;
|
|
}
|
|
NCV_SKIP_COND_END
|
|
|
|
if (bLoopVirgin)
|
|
{
|
|
rcode = true;
|
|
}
|
|
|
|
return rcode;
|
|
}
|
|
|
|
|
|
bool TestHaarCascadeLoader::deinit()
|
|
{
|
|
return true;
|
|
}
|